A decomposed subject-specific layer lets neural manifold models of fMRI capture individual spatial variation with far fewer parameters than per-subject layers.
Fast shared response model for fMRI data
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abstract
The shared response model provides a simple but effective framework to analyse fMRI data of subjects exposed to naturalistic stimuli. However when the number of subjects or runs is large, fitting the model requires a large amount of memory and computational power, which limits its use in practice. In this work, we introduce the FastSRM algorithm that relies on an intermediate atlas-based representation. It provides considerable speed-up in time and memory usage, hence it allows easy and fast large-scale analysis of naturalistic-stimulus fMRI data. Using four different datasets, we show that our method matches the performance of the original SRM algorithm while being about 5x faster and 20x to 40x more memory efficient. Based on this contribution, we use FastSRM to predict age from movie watching data on the CamCAN sample. Besides delivering accurate predictions (mean absolute error of 7.5 years), FastSRM extracts topographic patterns that are predictive of age, demonstrating that brain activity during free perception reflects age.
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Mapping minds not averages: a scalable subject-specific manifold learning framework for neuroimaging data
A decomposed subject-specific layer lets neural manifold models of fMRI capture individual spatial variation with far fewer parameters than per-subject layers.